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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">replication.R</h1>
<h4 class="author"><em>Tom</em></h4>
<h4 class="date"><em>Sun Nov 30 23:51:18 2014</em></h4>
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<pre class="r"><code># Replication materials ---------------------------------------------------
# Paper:   Estimating Uncertainty in Party Policy Positions 
#          Using the Confrontational Approach
# Authors: Tom Louwerse, Huib Pellikaan
# Contact: Tom.Louwerse@tcd.ie
# Dependencies: JAGS, script 'confrontational.R', 'confrontational_extra_
#               functions.R', packages rjags, foreach, plotrix

# Instructions for use ----------------------------------------------------
# 1. Make sure you have JAGS installed on your system
#    Download latest version from: 
#    http://sourceforge.net/projects/mcmc-jags/files/JAGS/
# 2. Install packages rjags, foreach and plotrix:
#    install.packages(c(&quot;rjags&quot;,&quot;foreach&quot;, &quot;plotrix&quot;))
# 3. Set working directory. All data and the 'confrontational.R' script
#    should be available in that directory.


# Working directory -------------------------------------------------------
# setwd(&quot;&quot;)

# Load packages -----------------------------------------------------------
library(foreach)

# Source confrontational function -----------------------------------------
source(&quot;confrontational.R&quot;)
source(&quot;confrontational_extra_functions.R&quot;)

# Load data ---------------------------------------------------------------
dta_nl &lt;- local(get(load(&quot;Data Confrontational NL 2010.RData&quot;)))
load(&quot;NL2010_comparison_data.RData&quot;)
load(&quot;Data EU.RData&quot;)


# Descriptives ------------------------------------------------------------
descriptive_data &lt;- foreach(i=1:length(dta_nl), .combine=rbind) %do% {
  data.frame(dimension=names(dta_nl)[i],n.parties=nrow(dta_nl[[i]]),n.items=ncol(dta_nl[[i]]), contra=table(dta_nl[[i]])[1], neutral=table(dta_nl[[i]])[2], pro=table(dta_nl[[i]])[3])
}

write.csv(descriptive_data, file=&quot;descriptives.csv&quot;)

# IRT model (NL) ----------------------------------------------------------
model_nl = foreach(i=1:length(dta_nl)) %do% confrontational(dta_nl[[i]], seed=5)</code></pre>
<pre><code>## Loading required package: lattice
## 
## Attaching package: 'coda'
## 
## The following object is masked _by_ '.GlobalEnv':
## 
##     traceplot
## 
## Linked to JAGS 3.4.0
## Loaded modules: basemod,bugs</code></pre>
<pre><code>## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model</code></pre>
<pre class="r"><code># Create plot (Figure 1)
windows(9,11)
par(mfrow=c(4,3), mar=c(2, 2, 3.5, 2))
for(i in 1:length(model_nl)) {
  plot(model_nl[[i]], main=names(dta_nl[i]))
} </code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" width="672" /></p>
<pre class="r"><code># Diagnostics -------------------------------------------------------------
foreach(i=1:length(model_nl)) %do% heidel.diag(model_nl[[i]]$actors)</code></pre>
<pre><code>## [[1]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed       1         0.316  
## PVV  passed       1         0.473  
## PvdA passed       1         0.897  
## CU   passed       1         0.609  
## GL   passed       1         0.998  
## PvdD passed       1         0.379  
## D66  passed       1         0.677  
## CDA  passed       1         0.761  
## SGP  passed       1         0.668  
## VVD  passed       1         0.571  
##                                 
##      Halfwidth Mean    Halfwidth
##      test                       
## SP   passed    -0.6840 0.00502  
## PVV  passed     2.1616 0.00529  
## PvdA passed    -0.8467 0.00563  
## CU   passed     0.1876 0.00535  
## GL   passed    -0.9947 0.00539  
## PvdD passed    -0.3097 0.00446  
## D66  passed    -0.6297 0.00484  
## CDA  passed    -0.0588 0.00507  
## SGP  passed     0.9221 0.00637  
## VVD  passed     0.2523 0.00597  
## 
## [[2]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed          1      0.929  
## PVV  passed          1      0.884  
## PvdA passed          1      0.136  
## CU   passed       4001      0.280  
## GL   passed          1      0.387  
## PvdD passed          1      0.330  
## D66  passed          1      0.747  
## CDA  passed          1      0.495  
## SGP  passed          1      0.229  
## VVD  passed          1      0.994  
##                                 
##      Halfwidth Mean    Halfwidth
##      test                       
## SP   passed    -1.1673 0.00649  
## PVV  passed     0.1029 0.00487  
## PvdA passed    -0.6063 0.00557  
## CU   passed    -0.1928 0.00808  
## GL   passed    -1.0832 0.00658  
## PvdD passed    -0.4481 0.00510  
## D66  passed     0.0853 0.00727  
## CDA  passed     0.5462 0.00584  
## SGP  passed     0.7202 0.00686  
## VVD  passed     2.0379 0.00631  
## 
## [[3]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed       1         0.477  
## PVV  passed       1         0.703  
## PvdA passed       1         0.622  
## CU   passed       1         0.877  
## GL   passed       1         0.188  
## PvdD passed       1         0.131  
## D66  passed       1         0.152  
## CDA  passed       1         0.546  
## SGP  passed       1         0.934  
## VVD  passed       1         0.101  
##                                
##      Halfwidth Mean   Halfwidth
##      test                      
## SP   passed    -0.265 0.00394  
## PVV  passed    -0.374 0.00412  
## PvdA passed    -0.205 0.00427  
## CU   passed     1.477 0.00581  
## GL   passed    -0.427 0.00401  
## PvdD passed    -0.378 0.00419  
## D66  passed    -0.995 0.00500  
## CDA  passed     0.232 0.00409  
## SGP  passed     1.922 0.00558  
## VVD  passed    -0.987 0.00492  
## 
## [[4]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   failed       NA        0.00663
## PVV  passed        1        0.34078
## PvdA passed        1        0.40215
## CU   passed        1        0.60493
## GL   passed        1        0.89817
## PvdD passed        1        0.76951
## D66  passed        1        0.22914
## CDA  passed        1        0.39062
## SGP  passed        1        0.42283
## VVD  passed        1        0.62645
##                                 
##      Halfwidth Mean    Halfwidth
##      test                       
## SP   &lt;NA&gt;           NA      NA  
## PVV  passed     1.1475 0.00781  
## PvdA passed    -0.3341 0.00511  
## CU   passed     0.2525 0.00532  
## GL   passed    -1.0154 0.00586  
## PvdD failed    -0.0247 0.00447  
## D66  passed    -1.8570 0.00601  
## CDA  passed     0.6305 0.00581  
## SGP  passed     0.6592 0.00519  
## VVD  passed     1.0769 0.00605  
## 
## [[5]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed       1         0.3558 
## PVV  passed       1         0.0732 
## PvdA passed       1         0.4085 
## CU   passed       1         0.2598 
## GL   passed       1         0.8204 
## PvdD passed       1         0.1222 
## D66  passed       1         0.5038 
## CDA  passed       1         0.1272 
## SGP  passed       1         0.7145 
## VVD  passed       1         0.9554 
##                                
##      Halfwidth Mean   Halfwidth
##      test                      
## SP   passed    -0.534 0.00459  
## PVV  passed    -0.751 0.00468  
## PvdA passed     0.183 0.00498  
## CU   passed     1.379 0.00729  
## GL   passed    -0.426 0.00463  
## PvdD passed    -0.536 0.00434  
## D66  passed    -0.753 0.00452  
## CDA  passed     1.765 0.00694  
## SGP  passed     0.641 0.00601  
## VVD  passed    -0.967 0.00461  
## 
## [[6]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed       1         0.9913 
## PVV  passed       1         0.6658 
## PvdA passed       1         0.0793 
## CU   passed       1         0.6554 
## GL   passed       1         0.0935 
## PvdD passed       1         0.6665 
## D66  passed       1         0.9145 
## CDA  passed       1         0.6118 
## SGP  passed       1         0.5173 
## VVD  passed       1         0.2092 
##                                 
##      Halfwidth Mean    Halfwidth
##      test                       
## SP   passed    -0.1954 0.00652  
## PVV  passed     2.3330 0.00527  
## PvdA failed     0.0219 0.00698  
## CU   failed     0.0159 0.00751  
## GL   passed    -0.9423 0.00628  
## PvdD passed    -0.8399 0.00668  
## D66  passed    -0.2030 0.00675  
## CDA  passed     0.5394 0.00804  
## SGP  failed    -0.0717 0.00856  
## VVD  passed    -0.6579 0.00617  
## 
## [[7]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed       1         0.8570 
## PVV  passed       1         0.2118 
## PvdA passed       1         0.5876 
## CU   passed       1         0.5972 
## GL   passed       1         0.4805 
## PvdD passed       1         0.9328 
## D66  passed       1         0.3156 
## CDA  passed       1         0.5530 
## SGP  passed       1         0.0563 
## VVD  passed       1         0.2004 
##                                 
##      Halfwidth Mean    Halfwidth
##      test                       
## SP   passed    -0.3087 0.00638  
## PVV  passed     1.2592 0.00775  
## PvdA passed    -0.9534 0.00620  
## CU   passed     0.4668 0.00758  
## GL   passed    -0.8323 0.00672  
## PvdD passed    -0.3137 0.00634  
## D66  passed    -1.1176 0.00680  
## CDA  passed     0.1766 0.00641  
## SGP  passed     1.7229 0.00700  
## VVD  passed    -0.0998 0.00661  
## 
## [[8]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed       1         0.415  
## PVV  passed       1         0.759  
## PvdA passed       1         0.652  
## CU   passed       1         0.228  
## GL   passed       1         0.867  
## PvdD passed       1         0.774  
## D66  passed       1         0.617  
## CDA  passed       1         0.528  
## SGP  passed       1         0.897  
## VVD  passed       1         0.676  
##                                  
##      Halfwidth Mean     Halfwidth
##      test                        
## SP   passed     0.50514 0.00397  
## PVV  passed     1.75931 0.00560  
## PvdA passed    -0.58178 0.00483  
## CU   passed     0.15121 0.00375  
## GL   passed    -1.45282 0.00628  
## PvdD failed     0.00199 0.00374  
## D66  passed    -1.36461 0.00634  
## CDA  passed    -0.18489 0.00460  
## SGP  passed     0.56520 0.00427  
## VVD  passed     0.60125 0.00479  
## 
## [[9]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed       1         0.5795 
## PVV  passed       1         0.5204 
## PvdA passed       1         0.6257 
## CU   passed       1         0.0704 
## GL   passed       1         0.4386 
## PvdD passed       1         0.5938 
## D66  passed       1         0.3787 
## CDA  passed       1         0.2538 
## SGP  passed       1         0.9121 
## VVD  passed       1         0.7024 
##                                 
##      Halfwidth Mean    Halfwidth
##      test                       
## SP   failed     0.0264 0.00528  
## PVV  passed    -1.0891 0.00817  
## PvdA passed     0.2841 0.00637  
## CU   passed     0.2370 0.00583  
## GL   passed     0.3770 0.00798  
## PvdD passed     0.5397 0.00614  
## D66  passed    -2.0788 0.00702  
## CDA  passed     0.3884 0.00590  
## SGP  passed     1.3266 0.00700  
## VVD  failed    -0.0115 0.00656  
## 
## [[10]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed          1      0.9226 
## PVV  passed          1      0.3267 
## PvdA passed          1      0.1760 
## CU   passed          1      0.5232 
## GL   passed       1001      0.0973 
## PvdD passed       3001      0.0590 
## D66  passed          1      0.1171 
## CDA  passed          1      0.6784 
## SGP  passed          1      0.4092 
## VVD  passed          1      0.5260 
##                                 
##      Halfwidth Mean    Halfwidth
##      test                       
## SP   passed     0.0866 0.00526  
## PVV  passed     1.7280 0.00577  
## PvdA failed     0.0404 0.00452  
## CU   passed    -0.7121 0.00722  
## GL   passed    -1.3302 0.00872  
## PvdD passed    -1.3331 0.00991  
## D66  passed    -0.1866 0.00504  
## CDA  passed     0.6269 0.00480  
## SGP  passed     0.5547 0.00539  
## VVD  passed     0.5233 0.00514  
## 
## [[11]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed          1      0.937  
## PVV  passed          1      0.445  
## PvdA passed          1      0.631  
## CU   passed       2001      0.181  
## GL   passed          1      0.615  
## PvdD passed          1      0.925  
## D66  passed          1      0.551  
## CDA  passed          1      0.275  
## SGP  passed          1      0.254  
## VVD  passed          1      0.541  
##                                
##      Halfwidth Mean   Halfwidth
##      test                      
## SP   passed    -0.989 0.00765  
## PVV  passed     0.576 0.01418  
## PvdA passed    -0.557 0.00782  
## CU   passed     0.482 0.01120  
## GL   passed    -0.758 0.00798  
## PvdD passed    -0.820 0.00918  
## D66  passed    -0.874 0.00835  
## CDA  passed     1.412 0.00902  
## SGP  passed     1.060 0.01019  
## VVD  passed     0.462 0.00999  
## 
## [[12]]
##                                    
##      Stationarity start     p-value
##      test         iteration        
## SP   passed          1      0.8291 
## PVV  passed       1001      0.0615 
## PvdA passed          1      0.3265 
## CU   passed          1      0.2957 
## GL   passed          1      0.9250 
## PvdD passed          1      0.1595 
## D66  passed          1      0.6719 
## CDA  passed          1      0.6780 
## SGP  passed          1      0.8923 
## VVD  passed          1      0.9320 
##                                
##      Halfwidth Mean   Halfwidth
##      test                      
## SP   passed    -1.711 0.00632  
## PVV  passed    -0.989 0.00663  
## PvdA passed    -0.441 0.00473  
## CU   passed    -0.301 0.00452  
## GL   passed    -0.114 0.00417  
## PvdD passed    -0.174 0.00464  
## D66  passed     0.782 0.00546  
## CDA  passed     0.713 0.00581  
## SGP  passed     0.631 0.00507  
## VVD  passed     1.602 0.00634</code></pre>
<pre class="r"><code>foreach(i=1:length(model_nl)) %do% raftery.diag(model_nl[[i]]$actors)</code></pre>
<pre><code>## [[1]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3667  3746         0.979     
##  PVV  2        3741  3746         0.999     
##  PvdA 2        3741  3746         0.999     
##  CU   2        3620  3746         0.966     
##  GL   2        3834  3746         1.020     
##  PvdD 2        3771  3746         1.010     
##  D66  2        3680  3746         0.982     
##  CDA  2        3741  3746         0.999     
##  SGP  2        3741  3746         0.999     
##  VVD  2        3802  3746         1.010     
## 
## 
## [[2]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3865  3746         1.03      
##  PVV  2        3771  3746         1.01      
##  PvdA 2        3897  3746         1.04      
##  CU   2        3897  3746         1.04      
##  GL   2        3834  3746         1.02      
##  PvdD 2        3865  3746         1.03      
##  D66  2        3929  3746         1.05      
##  CDA  2        3802  3746         1.01      
##  SGP  2        3710  3746         0.99      
##  VVD  2        3771  3746         1.01      
## 
## 
## [[3]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3710  3746         0.990     
##  PVV  2        3994  3746         1.070     
##  PvdA 2        3865  3746         1.030     
##  CU   2        3710  3746         0.990     
##  GL   2        3741  3746         0.999     
##  PvdD 2        3962  3746         1.060     
##  D66  2        3741  3746         0.999     
##  CDA  2        3834  3746         1.020     
##  SGP  2        3802  3746         1.010     
##  VVD  2        3931  3746         1.050     
## 
## 
## [[4]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3710  3746         0.990     
##  PVV  2        3865  3746         1.030     
##  PvdA 2        3741  3746         0.999     
##  CU   2        3710  3746         0.990     
##  GL   2        3865  3746         1.030     
##  PvdD 2        3741  3746         0.999     
##  D66  2        3741  3746         0.999     
##  CDA  2        3680  3746         0.982     
##  SGP  2        3771  3746         1.010     
##  VVD  2        3771  3746         1.010     
## 
## 
## [[5]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3741  3746         0.999     
##  PVV  2        3834  3746         1.020     
##  PvdA 2        3710  3746         0.990     
##  CU   2        3834  3746         1.020     
##  GL   2        3834  3746         1.020     
##  PvdD 2        3741  3746         0.999     
##  D66  2        3620  3746         0.966     
##  CDA  2        3834  3746         1.020     
##  SGP  2        3865  3746         1.030     
##  VVD  2        3834  3746         1.020     
## 
## 
## [[6]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3741  3746         0.999     
##  PVV  2        3834  3746         1.020     
##  PvdA 2        3620  3746         0.966     
##  CU   2        3771  3746         1.010     
##  GL   2        3802  3746         1.010     
##  PvdD 2        3802  3746         1.010     
##  D66  1        3726  3746         0.995     
##  CDA  2        3897  3746         1.040     
##  SGP  2        3741  3746         0.999     
##  VVD  2        3802  3746         1.010     
## 
## 
## [[7]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3802  3746         1.010     
##  PVV  2        3650  3746         0.974     
##  PvdA 2        3994  3746         1.070     
##  CU   2        3897  3746         1.040     
##  GL   2        3710  3746         0.990     
##  PvdD 2        3834  3746         1.020     
##  D66  2        3650  3746         0.974     
##  CDA  2        3590  3746         0.958     
##  SGP  3        4061  3746         1.080     
##  VVD  2        3835  3746         1.020     
## 
## 
## [[8]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3802  3746         1.010     
##  PVV  2        3947  3746         1.050     
##  PvdA 2        3710  3746         0.990     
##  CU   2        3741  3746         0.999     
##  GL   2        3802  3746         1.010     
##  PvdD 2        3929  3746         1.050     
##  D66  2        3680  3746         0.982     
##  CDA  2        3710  3746         0.990     
##  SGP  2        3680  3746         0.982     
##  VVD  2        3929  3746         1.050     
## 
## 
## [[9]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3771  3746         1.01      
##  PVV  2        3771  3746         1.01      
##  PvdA 2        3931  3746         1.05      
##  CU   2        3802  3746         1.01      
##  GL   3        4166  3746         1.11      
##  PvdD 2        3771  3746         1.01      
##  D66  2        3802  3746         1.01      
##  CDA  2        3771  3746         1.01      
##  SGP  2        3802  3746         1.01      
##  VVD  2        3865  3746         1.03      
## 
## 
## [[10]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3710  3746         0.990     
##  PVV  2        3771  3746         1.010     
##  PvdA 2        3802  3746         1.010     
##  CU   2        3771  3746         1.010     
##  GL   2        3590  3746         0.958     
##  PvdD 2        3741  3746         0.999     
##  D66  2        3680  3746         0.982     
##  CDA  2        3650  3746         0.974     
##  SGP  2        3897  3746         1.040     
##  VVD  2        3710  3746         0.990     
## 
## 
## [[11]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   2        3994  3746         1.070     
##  PVV  3        4028  3746         1.080     
##  PvdA 2        3834  3746         1.020     
##  CU   2        3994  3746         1.070     
##  GL   2        3771  3746         1.010     
##  PvdD 2        3834  3746         1.020     
##  D66  2        3741  3746         0.999     
##  CDA  2        3620  3746         0.966     
##  SGP  2        3771  3746         1.010     
##  VVD  2        3865  3746         1.030     
## 
## 
## [[12]]
## 
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95 
##                                             
##       Burn-in  Total Lower bound  Dependence
##       (M)      (N)   (Nmin)       factor (I)
##  SP   1        3726  3746         0.995     
##  PVV  2        3771  3746         1.010     
##  PvdA 2        3771  3746         1.010     
##  CU   2        3741  3746         0.999     
##  GL   2        3771  3746         1.010     
##  PvdD 2        3771  3746         1.010     
##  D66  2        3771  3746         1.010     
##  CDA  2        3962  3746         1.060     
##  SGP  2        3741  3746         0.999     
##  VVD  2        3788  3746         1.010</code></pre>
<pre class="r"><code>foreach(i=1:length(model_nl)) %do% geweke.diag(model_nl[[i]]$actors)</code></pre>
<pre><code>## [[1]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##        SP       PVV      PvdA        CU        GL      PvdD       D66 
##  0.004926  0.475530 -0.189892 -1.119103 -0.577229  1.377849  0.763945 
##       CDA       SGP       VVD 
## -0.015924 -0.557793  0.210881 
## 
## 
## [[2]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##        SP       PVV      PvdA        CU        GL      PvdD       D66 
##  0.146606  0.006075  1.508588  0.358685 -0.678641 -1.308356 -0.537884 
##       CDA       SGP       VVD 
##  1.796249 -1.309467  0.237427 
## 
## 
## [[3]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##      SP     PVV    PvdA      CU      GL    PvdD     D66     CDA     SGP 
##  0.2455 -0.7889  0.2916 -0.8934 -0.5525  1.5406 -1.2726 -0.5472  0.6552 
##     VVD 
##  1.4519 
## 
## 
## [[4]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##      SP     PVV    PvdA      CU      GL    PvdD     D66     CDA     SGP 
##  0.3357  1.4690 -0.1291  1.7507  0.6371 -0.7763 -0.2680 -1.4588 -1.0205 
##     VVD 
## -0.9246 
## 
## 
## [[5]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##      SP     PVV    PvdA      CU      GL    PvdD     D66     CDA     SGP 
## -0.1827 -0.2844  0.6751  1.8850 -0.4358  1.3572 -0.1256 -1.6921 -0.4016 
##     VVD 
## -0.7204 
## 
## 
## [[6]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##      SP     PVV    PvdA      CU      GL    PvdD     D66     CDA     SGP 
##  0.7224 -0.1761 -1.1355  0.4384  2.2587 -1.1288  0.2217  1.2679 -1.1260 
##     VVD 
## -0.6805 
## 
## 
## [[7]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##       SP      PVV     PvdA       CU       GL     PvdD      D66      CDA 
##  0.14910 -0.34633  0.03032 -0.86123 -0.95629 -0.67369  2.15051  0.48537 
##      SGP      VVD 
##  1.08278 -1.03068 
## 
## 
## [[8]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##      SP     PVV    PvdA      CU      GL    PvdD     D66     CDA     SGP 
##  0.4837  0.1047  0.9191 -0.6490  0.3975 -0.4232 -0.3172 -0.8648 -0.5057 
##     VVD 
##  0.6010 
## 
## 
## [[9]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##        SP       PVV      PvdA        CU        GL      PvdD       D66 
##  0.145583  0.015429  0.452520  1.148926  0.005525  1.722688 -0.824128 
##       CDA       SGP       VVD 
## -1.414530 -0.468065 -0.680452 
## 
## 
## [[10]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##       SP      PVV     PvdA       CU       GL     PvdD      D66      CDA 
## -0.01603  1.00615 -2.26277  1.01917 -2.44642  1.51624  1.53650 -0.30074 
##      SGP      VVD 
##  1.33601 -1.43222 
## 
## 
## [[11]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##      SP     PVV    PvdA      CU      GL    PvdD     D66     CDA     SGP 
##  1.0912 -1.5745 -1.0559  2.1643 -0.4582  0.2062 -0.2727 -0.8174  2.4099 
##     VVD 
## -1.3124 
## 
## 
## [[12]]
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##      SP     PVV    PvdA      CU      GL    PvdD     D66     CDA     SGP 
##  0.6902  1.9221 -1.8370 -1.1660  0.2025 -1.9580  0.3019  0.1533 -0.7924 
##     VVD 
##  1.0524</code></pre>
<pre class="r"><code># Comparison with CMP CIs -------------------------------------------------
# Select only dimensions on which we have priors from CHES
dta.set.select &lt;- match(names(dta.ConCMP.per.issue), names(dta_nl))

# Get correct order of parties in original dataset
dta.set.order &lt;- match(dta.ConCMP.per.issue[[1]]$Party, rownames(dta_nl[[1]]))

# Parse CMP result into a dataframe, so it can be plotted using plot.confrontational
dta.CMP.result &lt;- foreach(i=1:length(dta.ConCMP.per.issue)) %do%
  list(actors_summary=data.frame(mean=dta.ConCMP.per.issue[[i]]$CMP, 
             low=dta.ConCMP.per.issue[[i]]$CMP-(1.96 * dta.ConCMP.per.issue[[i]]$CMP_se),
             hi=dta.ConCMP.per.issue[[i]]$CMP+(1.96 * dta.ConCMP.per.issue[[i]]$CMP_se),
             row.names=dta.ConCMP.per.issue[[i]]$Party))

# Create plot (Figure 2)
windows(7, 10)
par(mfrow=c(3,2), mar=c(2, 2, 3, 2))
CMP.dimensions &lt;- c(&quot;Multiculturalism&quot;, &quot;Stateconomy&quot;, &quot;Traditional morality&quot;, &quot;Liberalism-Conservatism&quot;, &quot;Nationalism&quot;, 
                    &quot;Liberalism-Conservatism&quot;, &quot;EU&quot;, &quot;Environment&quot;, &quot;Militarism&quot;, &quot;State Services&quot;)
for(i in c(1,2,3,7,10)) {
  biplot(model_nl[[dta.set.select[i]]], dta.CMP.result[[i]], labelx=&quot;Confrontational Approach&quot;, labely=&quot;Manifesto Project&quot;, main=paste(names(dta_nl)[dta.set.select[i]], CMP.dimensions[i], sep=&quot; / &quot;))
}</code></pre>
<pre><code>## Loading required package: plotrix</code></pre>
<pre><code>## Warning: package 'plotrix' was built under R version 3.1.2</code></pre>
<pre class="r"><code># IRT model with priors ---------------------------------------------------

# Select only dimensions on which we have priors from CHES
dta.set.select &lt;- match(names(dta.ConCHES.per.issue), names(dta_nl))

# Get correct order of parties in original dataset
dta.set.order &lt;- match(dta.ConCHES.per.issue[[1]]$Party, rownames(dta_nl[[1]]))

# Run those models
model_nl_priors = foreach(i=1:length(dta.set.select)) %do% 
  confrontational(dta_nl[[dta.set.select[i]]][dta.set.order,],
                  priors=list(mean=dta.ConCHES.per.issue[[i]]$CHES,
                              se=dta.ConCHES.per.issue[[i]]$CHES_se),
                  seed=5)</code></pre>
<pre><code>## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model
## 
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 977
## 
## Initializing model</code></pre>
<pre class="r"><code># Compare CI width (models with and without priors) (reported in-text)
calcCIwidth &lt;- function(x,y) {
  p &lt;- merge(x,y,by=0)
  CI.width.normal &lt;- p$'97.5%.x' - p$'2.5%.x'
  CI.width.prior &lt;-  p$'97.5%.y' - p$'2.5%.y'
  return(data.frame(Party=p$Row.names,
                    CI.width.normal=CI.width.normal,
                    CI.width.prior=CI.width.prior,
                    Difference=CI.width.normal-CI.width.prior))
}

ci.width &lt;- foreach(i=1:length(model_nl_priors)) %do% 
  calcCIwidth(model_nl[[dta.set.select[i]]]$actors_summary, model_nl_priors[[i]]$actors_summary)
names(ci.width) &lt;- names(dta_nl)[dta.set.select]

ci.width.diff.av &lt;- foreach(i=1:length(ci.width), .combine=rbind) %do% 
  data.frame(Issue=names(ci.width)[i],
             Mean_Difference=mean(ci.width[[i]]$Difference),
             Perc_Difference=mean(ci.width[[i]]$Difference)/mean(ci.width[[i]]$CI.width.normal) * 100)

print( ci.width.diff.av)</code></pre>
<pre><code>##                Issue Mean_Difference Perc_Difference
## 1            Culture          0.3276           31.40
## 2            Economy          0.4280           37.16
## 3           Religion          0.2694           30.19
## 4      Law and Order          0.3822           35.88
## 5      Communitarian          0.2913           28.17
## 6     Dutch Identity          0.3358           25.31
## 7      Authoritarian          0.5580           42.15
## 8     EU Integration          0.3717           39.51
## 9  Green Environment          0.3748           32.11
## 10    Foreign Policy          0.5584           33.75
## 11    Social Welfare          0.3356           33.19</code></pre>
<pre class="r"><code># Correlations between models with and without priors (reported in-text)

corModels &lt;- function(x,y,method=&quot;pearson&quot;) {
  p &lt;- merge(x,y,by=0)
  return(cor(p$x,p$y, method=method))
}

ci.cor.r &lt;- foreach(i=1:length(model_nl_priors), .combine=c) %do% corModels(model_nl[[dta.set.select[i]]]$actors_summary[,1], model_nl_priors[[i]]$actors_summary[,1])
names(ci.cor.r) &lt;- names(dta_nl)[dta.set.select]

ci.cor.tau &lt;- foreach(i=1:length(model_nl_priors), .combine=c) %do% corModels(model_nl[[dta.set.select[i]]]$actors_summary[,1], model_nl_priors[[i]]$actors_summary[,1],method=&quot;kendall&quot;)
names(ci.cor.tau) &lt;- names(dta_nl)[dta.set.select]

print(ci.cor.r)</code></pre>
<pre><code>##           Culture           Economy          Religion     Law and Order 
##            0.9174            0.9549            0.9716            0.9790 
##     Communitarian    Dutch Identity     Authoritarian    EU Integration 
##            0.8404            0.8178            0.9651            0.9813 
## Green Environment    Foreign Policy    Social Welfare 
##            0.9778            0.8430            0.9691</code></pre>
<pre class="r"><code>print(ci.cor.tau)</code></pre>
<pre><code>##           Culture           Economy          Religion     Law and Order 
##            0.7333            0.8667            0.8667            0.9556 
##     Communitarian    Dutch Identity     Authoritarian    EU Integration 
##            0.6889            0.6000            0.9556            0.8222 
## Green Environment    Foreign Policy    Social Welfare 
##            0.8667            0.6000            0.6889</code></pre>
<pre class="r"><code># Different specifications for null-scores --------------------------------

# Create dataset with zero values as missing (NA)
cul &lt;- dta_nl[[1]]
cul[which(cul==2, arr.ind=TRUE)] &lt;- NA

out_org &lt;- confrontational(dta_nl[[1]],seed=5)</code></pre>
<pre><code>## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model</code></pre>
<pre class="r"><code>out_sum &lt;- (rowSums(dta_nl[[1]])-20)
out_mar &lt;- confrontational(cul,seed=5)</code></pre>
<pre><code>## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 957
## 
## Initializing model</code></pre>
<pre class="r"><code>out_leg_cut &lt;- confrontational(dta_nl[[1]], model.file=&quot;jags_model_actor_cut.bug&quot;,seed=5)</code></pre>
<pre><code>## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 1157
## 
## Initializing model</code></pre>
<pre class="r"><code># Use function compareplot.confrontational
compareplot.confrontational(out_org, out_mar, out_leg_cut)</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" width="672" /></p>
<pre class="r"><code># IRT Model (EU) ----------------------------------------------------------

# Run model
model_eu &lt;- confrontational(eu_dta, seed=5)</code></pre>
<pre><code>## Data contains values other than 1, 2 and 3. Data will be assumed to be in ordinal form and will be recoded.
## Header of recoded dataframe:
##          X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10
## FN [FR]   1  1  1  2  2  2  1  2  1   3  1  1  2  1  2  2  2  1  3   2
## PS [FR]   3  3  3  3  3  3  3  3  2   3  3  3  2  3  2  3  2  3  3   2
## UMP [FR]  3  3  3  3  3  1  1  3  2   3  3  3  3  1  2  3  3  3  2   2
## EE [FR]   3  3  2  3  3  3  3  3  3   3  3  2  3  3  3  3  2  3  3   2
## MD [FR]   3  3  2  3  3  3  3  3  3   3  3  3  3  3  2  3  3  3  2   2
## FG [FR]   3  1  1  3  2  3  2  3  3   2  3  2  3  3  2  2  2  1  3   2
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
##    Graph Size: 3707
## 
## Initializing model</code></pre>
<pre class="r"><code># Create plot (Figure 4)
windows(7,7)
par(mfcol=c(1,1),  mar=c(2, 2, 3, 2))
plot(model_eu)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" width="672" /></p>
<pre class="r"><code># Scores per party group (not reported)
partygroups = list()
partygroupscores = matrix(NA,ncol=3,nrow=length(levels(eu_metadata$PARTYGROUP)))
partygroupnames = levels(eu_metadata$PARTYGROUP)
rownames(partygroupscores) = partygroupnames
colnames(partygroupscores) = c(&quot;Mean&quot;, &quot;2.5%&quot;, &quot;97.5%&quot;)
for(i in 1:length(levels(eu_metadata$PARTYGROUP))) {
  partygroups[[i]] = which(eu_metadata$PARTYGROUP==levels(eu_metadata$PARTYGROUP)[i])
  sc = apply(model_eu$actors, 1, function(x) mean(x[partygroups[[i]]]))
  partygroupscores[i,] = c(mean(sc), quantile(sc, 0.025), quantile(sc, 0.975))
}
print(partygroupscores)</code></pre>
<pre><code>##          Mean      2.5%   97.5%
## ALDE   0.4266  0.189649  0.6624
## ECR    0.2692 -0.196270  0.8779
## EFD    0.2660 -0.206203  0.8688
## EPP    0.3973  0.062677  0.7576
## Green  0.7456  0.276466  1.2753
## Left  -0.7572 -1.035463 -0.4857
## NI    -2.0268 -2.265179 -1.7107
## S&amp;D    0.3247  0.009376  0.6749</code></pre>
<pre class="r"><code># Scores per country (means and standard deviations)
france = which(eu_metadata$COUNTRY==&quot;FR&quot;)
ireland = which(eu_metadata$COUNTRY==&quot;IR&quot;)
netherlands = which(eu_metadata$COUNTRY==&quot;NL&quot;)
countryscores = apply(model_eu$actors, 1, function(x) 
  c(weighted.mean(x[france],eu_metadata$SEATS[france]),
    weighted.mean(x[netherlands],eu_metadata$SEATS[netherlands]),
    weighted.mean(x[ireland],eu_metadata$SEATS[ireland])))
countryscores_sd = apply(model_eu$actors, 1, function(x) 
  c(sd(x[france]),sd(x[netherlands]),sd(x[ireland])))

# Calculate summary statistics, save in dataframe ready for plotting
country_means &lt;- list()
country_means$actors_summary = t(apply(countryscores, 1, function(x) c(mean(x), quantile(x, 0.025), quantile(x, 0.975))))
rownames(country_means$actors_summary) = c(&quot;France&quot;, &quot;Netherlands&quot;, &quot;Ireland&quot;)

country_sds &lt;- list()
country_sds$actors_summary = t(apply(countryscores_sd, 1, function(x) c(mean(x), quantile(x, 0.025), quantile(x, 0.975))))
rownames(country_sds$actors_summary) = c(&quot;France&quot;, &quot;Netherlands&quot;, &quot;Ireland&quot;)

windows(7,7)
# Plot (Figure 4)
par(mfcol=c(2,1),mar=c(2.1,4.1,2.1,2.1))
plot.confrontational(country_means,labelcex=1)
title(ylab=&quot;Mean&quot;)
plot.confrontational(country_sds,labelcex=1)
title(ylab=&quot;Standard deviation&quot;)</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" width="672" /></p>
<pre class="r"><code># Calculate posterior probabilities that country score row is higher than country score column (reported in-text)
countryscores_matrix = matrix(NA, nrow(countryscores),nrow(countryscores))
countryscores_sd_matrix = matrix(NA, nrow(countryscores),nrow(countryscores))

for(i in 1:nrow(countryscores)) {
  for(j in 1:nrow(countryscores)) {
    countryscores_matrix[i,j] &lt;- sum(countryscores[i,] &lt; countryscores[j,])/length(countryscores[i,])
    countryscores_sd_matrix[i,j] &lt;- sum(countryscores_sd[i,] &lt; countryscores_sd[j,])/length(countryscores_sd[i,])
  }
}
rownames(countryscores_matrix) = colnames(countryscores_matrix) = c(&quot;France&quot;, &quot;Netherlands&quot;, &quot;Ireland&quot;) 
rownames(countryscores_sd_matrix) = colnames(countryscores_sd_matrix) = c(&quot;France&quot;, &quot;Netherlands&quot;, &quot;Ireland&quot;) 

print(countryscores_matrix)</code></pre>
<pre><code>##             France Netherlands Ireland
## France      0.0000      0.2693  0.1823
## Netherlands 0.7307      0.0000  0.4937
## Ireland     0.8177      0.5063  0.0000</code></pre>
<pre class="r"><code>print(countryscores_sd_matrix)</code></pre>
<pre><code>##             France Netherlands Ireland
## France      0.0000      0.9998  0.0558
## Netherlands 0.0002      0.0000  0.0000
## Ireland     0.9442      1.0000  0.0000</code></pre>


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